Turn Your AI Agent Into a Data Analyst: 5 ClawHub Skills for Data Work

Data work has always been the domain of specialists — analysts who know SQL, data scientists who can write pandas, visualization experts who can configure a chart properly. That specialization made sense when the tools required deep expertise to use.

ClawHub's data skills change that calculus. They give Claude Code real analytical capability: the ability to query databases, process CSV files, pull market data, and produce charts — without you needing to write a line of Python or remember Tableau keyboard shortcuts.

This roundup covers the five data skills that consistently deliver real value in production workflows.

1. Stock Analysis

For anyone who needs to track market data — whether you're running a fintech application, monitoring portfolio performance, or just want to stay informed — the stock analysis skill gives Claude Code direct access to market data feeds and the analytical tools to make sense of them.

What it does:

• Pulls real-time and historical price data
• Calculates technical indicators (moving averages, RSI, MACD, Bollinger Bands)
• Runs comparative analysis across multiple tickers
• Generates plain-language summaries of market conditions
• Tracks earnings dates and news events affecting positions

Install:

/skills install stock-analysis

Usage example:

You: Analyze NVDA over the last 90 days and give me a summary
Agent: NVDA — 90-day analysis
       Price change: +18.4% ($612 → $724)
       RSI (14): 67 — approaching overbought territory
       50-day MA: $694 (price trading above — bullish signal)
       200-day MA: $641 (sustained uptrend confirmed)
       Notable events: Earnings beat Jan 28 (+9% gap up), GTC keynote
       announcement Feb 14 (+4.2%)
       Outlook: Strong momentum, watch for RSI normalization near $740-750

Limitation worth knowing: This skill provides data and analysis, not financial advice. Output is for informational purposes. The skill itself includes a disclaimer on every analysis output, which you can't disable — and shouldn't want to.

2. CSV Processor

The workhorse of the data skills. Almost every data source eventually exports to CSV. The CSV processor skill handles files that would take hours to analyze manually and returns structured insights in minutes.

What it does:

• Reads and parses CSV files of any size (tested up to 500MB)
• Identifies column types, data quality issues, and outliers automatically
• Runs statistical summaries (mean, median, percentiles, distributions)
• Filters, sorts, and transforms data based on plain-language instructions
• Merges and joins multiple CSV files
• Exports processed data in CSV, JSON, or markdown table formats

Install:

/skills install csv-processor

Usage example:

You: Analyze sales_q4_2025.csv and tell me which product lines underperformed
Agent: Loaded 12,847 rows across 8 columns.

       Underperforming product lines (below category average):
       — Hardware accessories: $234,891 revenue vs $412,000 category avg (-43%)
       — Legacy software licenses: $89,234 vs $156,000 avg (-43%)
       — Professional services: $1.2M vs $1.8M avg (-33%)

       Possible cause: Hardware accessories missing Nov-Dec data (2,341 rows
       with null revenue values). Recommend verifying data completeness
       before drawing conclusions.

That last observation — catching the null values — is where the skill earns its place. A human analyst in a hurry would miss it. The skill flags it automatically.

Limitation worth knowing: Complex multi-step transformations (pivot tables, conditional aggregations across multiple dimensions) occasionally require explicit instruction. The skill handles most cases well but sometimes interprets ambiguous requests differently than you expect. Be specific about what you want.

3. Database Query Assistant

Direct database access with natural language querying. The database skill connects to PostgreSQL, MySQL, and SQLite databases and lets you ask questions in plain English rather than SQL.

What it does:

• Connects to your database using standard connection strings
• Translates natural language questions into SQL queries
• Shows you the generated SQL before executing (configurable)
• Returns formatted results with optional summary analysis
• Identifies slow queries and suggests index optimizations
• Generates schema documentation from your database structure

Install:

/skills install database-query

Setup:

/skills configure database-query --connection "postgresql://user:pass@host:5432/dbname"

Usage example:

You: Which users signed up in the last 30 days but never made a purchase?
Agent: Generated SQL:
       SELECT u.email, u.created_at, u.acquisition_source
       FROM users u
       LEFT JOIN orders o ON u.id = o.user_id
       WHERE u.created_at > NOW() - INTERVAL '30 days'
       AND o.id IS NULL
       ORDER BY u.created_at DESC;

       Results: 1,247 users (34% of signups from last 30 days)
       Top acquisition sources for this segment: paid_social (41%),
       organic_search (29%), referral (18%)

       Recommendation: This cohort is ripe for onboarding email sequence.
       High paid acquisition cost with no conversion — retention priority.

Limitation worth knowing: The skill has read-only mode enabled by default, and you should keep it that way unless you're running specific write workflows you've explicitly designed. Natural language write queries are a footgun. Read-only first.

4. Data Visualization

The skill that turns your data outputs into actual charts. Works in conjunction with the CSV processor and database query skills to visualize results, or operates standalone when you provide data directly.

What it generates:

• Line charts (time series, trends)
• Bar and column charts (comparisons, rankings)
• Scatter plots (correlations, distributions)
• Pie and donut charts (composition)
• Heatmaps (frequency, correlation matrices)

Output formats: PNG, SVG, or embedded HTML with interactive charts via Chart.js.

Install:

/skills install data-visualization

Usage example:

You: Visualize the monthly revenue trend from the CSV results
Agent: Creating line chart: Monthly Revenue Trend (Jan-Dec 2025)

       Generated: monthly_revenue_trend.png
       Key insight annotated: Q3 dip (Jul-Aug) corresponds with
       product launch delay mentioned in your notes

Limitation worth knowing: The skill generates static charts well. Interactive dashboards require the HTML output mode plus a web environment to serve them. Don't expect Tableau functionality — expect fast, clean charts that communicate a single insight clearly.

5. The Data Stack in Practice

These four skills work best as a pipeline:

Raw data source (CSV / database)
        ↓
  CSV Processor or Database Query Assistant
  (clean, filter, aggregate)
        ↓
  Data Visualization
  (chart the finding)
        ↓
  Stock Analysis (if financial context needed)
  (add market benchmarking)

A real example: a SaaS company uses this stack every Monday morning. The CSV processor pulls the weekend's user activity export, the database query assistant checks conversion rates against the user cohort, and the visualization skill generates the weekly metrics chart that goes into the Slack standup. Total time: 8 minutes. It used to take an analyst 90 minutes.

That time savings compounds. Four hours a month back to do work that actually requires judgment. That's what good data skills are worth.